Multiobjective Optimized Dispatching for Integrated Energy System Based on Hierarchical Progressive Parallel NSGA-II Algorithm

Abstract

Considering the importance of reducing system operating costs and controlling pollutant emissions by optimizing the operation of the integrated energy system, the energy supply structure of the integrated energy system and the joint multiobjective optimization dispatching structure is analyzed in this paper based on a day-ahead economic optimization dispatching model of the integrated energy system. Afterwards, the multiobjective optimization model of the integrated energy system is studied and multiobjective hierarchical progressive parallel algorithm based on improved NSGA-II is proposed according to the characteristics of the model. The algorithm improves the nondominated layer sorting algorithm, changes the convergence judgment condition while introducing the target reaching method to accelerate convergence, and introduces parallel computing technology according to the characteristics of the algorithm. The case shows that the proposed algorithm not only has advantages on the diversity in searching solutions but also can achieve better results in many aspects such as the iteration time and algorithm convergence which are required in practical engineering projects.

Nonlinear multiobjective and dynamic real‐time predictive optimization for optimal operation of baseload power plants under variable renewable energy

“…Each individual term has been defined in previous work 22 and it is also included in Appendix S1. As this study includes costs from different years, all cost values are scaled considering the 2019 Chemical Engineering Plant Cost Index (CEPCI) 42 and normalized to the same basis chosen as year 2000, as shown in Equation (19).…”

“…Then, a single compromise is selected from the Pareto front to be implemented in real time. This strategy is applied in different fields of energy systems such as the dispatching and integration of energy systems, 19 simulation and optimization of a CO 2 carbon capture coal‐fired power plant, 20 as well as in the modeling of a lithium‐ion battery 21 . The main benefit of this approach is the information availability of every Pareto‐optimal compromise between multiple objectives.…”

Energy Dispatching Based on an Improved PSO-ACO Algorithm

“…Aiming at the problem of energy dispatching route time, Ellahi and Abbas [31] proposed a hybrid metaheuristic approach to alleviate the energy dispatching problem of power plants, which reduces the cost reasonably and shortens the calculation time. Zeng et al [32] proposed a multiobjective dispatch approach based on hierarchical progressive parallel NSGA-II algorithm, which speeds up the convergence speed of energy dispatching and reduces the algorithm iteration time. Li and Wang [33] proposed an improved intensity pareto evolutionary algorithm 2 (ISPEA2) and improved nondominated sorting genetic algorithm 2 (INSGA2), which improve the accuracy of energy dispatching and reduce the dispatching time.…”